Predator – Prey Dynamics and the Red Queen Hypothesis
the real-world attributes of various species are much more complicated and nuanced than their simulated counterparts in our ABM . However , it is difficult to tease out which of these additional attributes are important and which are inconsequential . A generative model allows us to add complexity one layer at a time and determine , from a simulation standpoint , which factors are most important , and can therefore give us a better understanding of which factors are also most important in nature .
Therefore , the assumptions encoded in this model are relatively straightforward . The food is grown on each patch based on a “ slider ” in the user interface ; as the slider is increased , the amount of food grown per patch , per simulation time-step , increases in a linear fashion . The prey agents perform three basic functions during each turn : they move at random , eat if there is food present , and increase their age . They also reproduce asexually as a linear function of how much food they have consumed . A slider could control the threshold for reproduction . However , this linear relationship cannot be altered in this simulation . The predators are the same : they move at random , eat if there is prey available , and increase their age during each turn . Note that both the prey and the predators have a slider labeled “ turns per tick .” This allows for additional control and experimentation , in that the number of turns for each species during each simulation time step can be altered , which changes the number of actions performed during each “ tick ,” relative to the other populations . The consequences of changing this controller are discussed below . As with the prey , the predators reproduce asexually as a function of the total amount of food ( in this case , prey ) that is consumed . In some experiments , also a “ top predator ” consumes the predator in the same way that the predator consumes the prey . This changes the dynamics of all the populations in various ways ; however , only the simple three-level model is discussed here . All models were created using the NetLogo modeling environment ( Carmichael & Hadzikadic , 2013 ).
2.2 - The Red Queen Hypothesis
The Red Queen hypothesis was first introduced in 1973 ( Wilensky , 1999 ). It expresses the idea of an “ arms race ” between antagonistic species , such as predators and prey , in a common ecosystem . Given the intuitive benefits of increased efficiency for a member of one species , it seems likely that an advantage in the phenotype would ensure that the related genotype would more likely survive and spread on evolutionary time scales throughout the population . However if , for example , the predators become better hunters , then there is a subsequent pressure on the prey to also adapt in order to better survive . Once the prey adopts better survival techniques , then the predators adapt again , and the cycle continues .
Given this theory , the question naturally arises : why do species not continually increase in efficiency ? There must be some mechanism that reduces or even eliminates continued evolutionary advantage . The first and most intuitive answer is that there is a cost associated with efficiency increases and , at some point along a continuum ; this cost is greater than the additional benefit . Related to this idea is the natural diversity in abilities across the prey species . In particular , individual prey that are very young , very old , sick , or injured might generally have less ability than those in their prime . If this is the case , then an individual predator need not work much harder to experience an increase in the number of vulnerable prey who is susceptible to predation .
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